Overview

Dataset statistics

Number of variables15
Number of observations8793
Missing cells3
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory150.1 B

Variable types

Text8
Categorical2
DateTime1
Numeric4

Alerts

age_on_netflix is highly overall correlated with release_yearHigh correlation
release_year is highly overall correlated with age_on_netflixHigh correlation
show_id has unique valuesUnique
title has unique valuesUnique
age_on_netflix has 3240 (36.8%) zerosZeros

Reproduction

Analysis started2025-11-26 14:13:00.294766
Analysis finished2025-11-26 14:13:04.655840
Duration4.36 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

show_id
Text

Unique 

Distinct8793
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size395.4 KiB
2025-11-26T14:13:05.221160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.8741044
Min length2

Characters and Unicode

Total characters42858
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8793 ?
Unique (%)100.0%

Sample

1st rows1
2nd rows2
3rd rows3
4th rows4
5th rows5
ValueCountFrequency (%)
s101
 
< 0.1%
s88071
 
< 0.1%
s11
 
< 0.1%
s21
 
< 0.1%
s31
 
< 0.1%
s41
 
< 0.1%
s51
 
< 0.1%
s61
 
< 0.1%
s71
 
< 0.1%
s87921
 
< 0.1%
Other values (8783)8783
99.9%
2025-11-26T14:13:05.953811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s8793
20.5%
43659
8.5%
23658
8.5%
33657
8.5%
13657
8.5%
53656
8.5%
63653
8.5%
73649
8.5%
83368
 
7.9%
92555
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)42858
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s8793
20.5%
43659
8.5%
23658
8.5%
33657
8.5%
13657
8.5%
53656
8.5%
63653
8.5%
73649
8.5%
83368
 
7.9%
92555
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)42858
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s8793
20.5%
43659
8.5%
23658
8.5%
33657
8.5%
13657
8.5%
53656
8.5%
63653
8.5%
73649
8.5%
83368
 
7.9%
92555
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)42858
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s8793
20.5%
43659
8.5%
23658
8.5%
33657
8.5%
13657
8.5%
53656
8.5%
63653
8.5%
73649
8.5%
83368
 
7.9%
92555
 
6.0%

type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size395.4 KiB
Movie
6129 
TV Show
2664 

Length

Max length7
Median length5
Mean length5.6059365
Min length5

Characters and Unicode

Total characters49293
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMovie
2nd rowTV Show
3rd rowTV Show
4th rowTV Show
5th rowTV Show

Common Values

ValueCountFrequency (%)
Movie6129
69.7%
TV Show2664
30.3%

Length

2025-11-26T14:13:06.092650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T14:13:06.171674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
movie6129
53.5%
tv2664
23.3%
show2664
23.3%

Most occurring characters

ValueCountFrequency (%)
o8793
17.8%
M6129
12.4%
v6129
12.4%
i6129
12.4%
e6129
12.4%
T2664
 
5.4%
V2664
 
5.4%
2664
 
5.4%
S2664
 
5.4%
h2664
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)49293
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o8793
17.8%
M6129
12.4%
v6129
12.4%
i6129
12.4%
e6129
12.4%
T2664
 
5.4%
V2664
 
5.4%
2664
 
5.4%
S2664
 
5.4%
h2664
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)49293
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o8793
17.8%
M6129
12.4%
v6129
12.4%
i6129
12.4%
e6129
12.4%
T2664
 
5.4%
V2664
 
5.4%
2664
 
5.4%
S2664
 
5.4%
h2664
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)49293
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o8793
17.8%
M6129
12.4%
v6129
12.4%
i6129
12.4%
e6129
12.4%
T2664
 
5.4%
V2664
 
5.4%
2664
 
5.4%
S2664
 
5.4%
h2664
 
5.4%

title
Text

Unique 

Distinct8793
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size395.4 KiB
2025-11-26T14:13:06.725493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length104
Median length71
Mean length17.720573
Min length1

Characters and Unicode

Total characters155817
Distinct characters200
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8793 ?
Unique (%)100.0%

Sample

1st rowDick Johnson Is Dead
2nd rowBlood & Water
3rd rowGanglands
4th rowJailbirds New Orleans
5th rowKota Factory
ValueCountFrequency (%)
the2228
 
8.1%
of707
 
2.6%
a352
 
1.3%
in288
 
1.1%
263
 
1.0%
and235
 
0.9%
to199
 
0.7%
love170
 
0.6%
my143
 
0.5%
2129
 
0.5%
Other values (9165)22634
82.8%
2025-11-26T14:13:08.151488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18549
 
11.9%
e14530
 
9.3%
a11151
 
7.2%
o8859
 
5.7%
i8530
 
5.5%
r8254
 
5.3%
n8115
 
5.2%
t7115
 
4.6%
s6179
 
4.0%
h5443
 
3.5%
Other values (190)59092
37.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)155817
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18549
 
11.9%
e14530
 
9.3%
a11151
 
7.2%
o8859
 
5.7%
i8530
 
5.5%
r8254
 
5.3%
n8115
 
5.2%
t7115
 
4.6%
s6179
 
4.0%
h5443
 
3.5%
Other values (190)59092
37.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)155817
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18549
 
11.9%
e14530
 
9.3%
a11151
 
7.2%
o8859
 
5.7%
i8530
 
5.5%
r8254
 
5.3%
n8115
 
5.2%
t7115
 
4.6%
s6179
 
4.0%
h5443
 
3.5%
Other values (190)59092
37.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)155817
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18549
 
11.9%
e14530
 
9.3%
a11151
 
7.2%
o8859
 
5.7%
i8530
 
5.5%
r8254
 
5.3%
n8115
 
5.2%
t7115
 
4.6%
s6179
 
4.0%
h5443
 
3.5%
Other values (190)59092
37.9%
Distinct4528
Distinct (%)51.5%
Missing0
Missing (%)0.0%
Memory size395.4 KiB
2025-11-26T14:13:09.161682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length208
Median length179
Mean length12.87672
Min length2

Characters and Unicode

Total characters113225
Distinct characters106
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3660 ?
Unique (%)41.6%

Sample

1st rowKirsten Johnson
2nd rowUnknown
3rd rowJulien Leclercq
4th rowUnknown
5th rowUnknown
ValueCountFrequency (%)
unknown2621
 
15.1%
david122
 
0.7%
michael117
 
0.7%
john90
 
0.5%
paul74
 
0.4%
robert56
 
0.3%
chris52
 
0.3%
peter52
 
0.3%
daniel49
 
0.3%
james48
 
0.3%
Other values (6502)14029
81.0%
2025-11-26T14:13:10.587691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n14096
 
12.4%
a10127
 
8.9%
8517
 
7.5%
o7550
 
6.7%
e7046
 
6.2%
i5919
 
5.2%
r5609
 
5.0%
k4066
 
3.6%
l3882
 
3.4%
h3302
 
2.9%
Other values (96)43111
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)113225
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n14096
 
12.4%
a10127
 
8.9%
8517
 
7.5%
o7550
 
6.7%
e7046
 
6.2%
i5919
 
5.2%
r5609
 
5.0%
k4066
 
3.6%
l3882
 
3.4%
h3302
 
2.9%
Other values (96)43111
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)113225
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n14096
 
12.4%
a10127
 
8.9%
8517
 
7.5%
o7550
 
6.7%
e7046
 
6.2%
i5919
 
5.2%
r5609
 
5.0%
k4066
 
3.6%
l3882
 
3.4%
h3302
 
2.9%
Other values (96)43111
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)113225
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n14096
 
12.4%
a10127
 
8.9%
8517
 
7.5%
o7550
 
6.7%
e7046
 
6.2%
i5919
 
5.2%
r5609
 
5.0%
k4066
 
3.6%
l3882
 
3.4%
h3302
 
2.9%
Other values (96)43111
38.1%

cast
Text

Distinct7680
Distinct (%)87.3%
Missing0
Missing (%)0.0%
Memory size395.4 KiB
2025-11-26T14:13:11.464543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length771
Median length367
Mean length109.18276
Min length3

Characters and Unicode

Total characters960044
Distinct characters156
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7514 ?
Unique (%)85.5%

Sample

1st rowUnknown
2nd rowAma Qamata, Khosi Ngema, Gail Mabalane, Thabang Molaba, Dillon Windvogel, Natasha Thahane, Arno Greeff, Xolile Tshabalala, Getmore Sithole, Cindy Mahlangu, Ryle De Morny, Greteli Fincham, Sello Maake Ka-Ncube, Odwa Gwanya, Mekaila Mathys, Sandi Schultz, Duane Williams, Shamilla Miller, Patrick Mofokeng
3rd rowSami Bouajila, Tracy Gotoas, Samuel Jouy, Nabiha Akkari, Sofia Lesaffre, Salim Kechiouche, Noureddine Farihi, Geert Van Rampelberg, Bakary Diombera
4th rowUnknown
5th rowMayur More, Jitendra Kumar, Ranjan Raj, Alam Khan, Ahsaas Channa, Revathi Pillai, Urvi Singh, Arun Kumar
ValueCountFrequency (%)
unknown825
 
0.6%
michael650
 
0.5%
david560
 
0.4%
john557
 
0.4%
lee458
 
0.3%
james419
 
0.3%
kim350
 
0.3%
paul345
 
0.3%
de288
 
0.2%
khan263
 
0.2%
Other values (33156)127943
96.4%
2025-11-26T14:13:13.917264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
123872
 
12.9%
a94870
 
9.9%
e65620
 
6.8%
n60024
 
6.3%
i56404
 
5.9%
,56051
 
5.8%
r48303
 
5.0%
o45303
 
4.7%
l35296
 
3.7%
h28503
 
3.0%
Other values (146)345798
36.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)960044
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
123872
 
12.9%
a94870
 
9.9%
e65620
 
6.8%
n60024
 
6.3%
i56404
 
5.9%
,56051
 
5.8%
r48303
 
5.0%
o45303
 
4.7%
l35296
 
3.7%
h28503
 
3.0%
Other values (146)345798
36.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)960044
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
123872
 
12.9%
a94870
 
9.9%
e65620
 
6.8%
n60024
 
6.3%
i56404
 
5.9%
,56051
 
5.8%
r48303
 
5.0%
o45303
 
4.7%
l35296
 
3.7%
h28503
 
3.0%
Other values (146)345798
36.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)960044
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
123872
 
12.9%
a94870
 
9.9%
e65620
 
6.8%
n60024
 
6.3%
i56404
 
5.9%
,56051
 
5.8%
r48303
 
5.0%
o45303
 
4.7%
l35296
 
3.7%
h28503
 
3.0%
Other values (146)345798
36.0%

country
Text

Distinct748
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size395.4 KiB
2025-11-26T14:13:14.472888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length123
Median length104
Mean length12.622882
Min length4

Characters and Unicode

Total characters110993
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique563 ?
Unique (%)6.4%

Sample

1st rowUnited States
2nd rowSouth Africa
3rd rowUnited States
4th rowUnited States
5th rowIndia
ValueCountFrequency (%)
united5355
32.1%
states4513
27.0%
india1046
 
6.3%
kingdom805
 
4.8%
canada445
 
2.7%
france393
 
2.4%
japan316
 
1.9%
south293
 
1.8%
germany232
 
1.4%
spain232
 
1.4%
Other values (126)3075
18.4%
2025-11-26T14:13:15.283345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t15345
13.8%
e12187
11.0%
a10996
9.9%
n10346
9.3%
i9411
8.5%
d8102
 
7.3%
7912
 
7.1%
U5376
 
4.8%
S5177
 
4.7%
s5102
 
4.6%
Other values (41)21039
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)110993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t15345
13.8%
e12187
11.0%
a10996
9.9%
n10346
9.3%
i9411
8.5%
d8102
 
7.3%
7912
 
7.1%
U5376
 
4.8%
S5177
 
4.7%
s5102
 
4.6%
Other values (41)21039
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)110993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t15345
13.8%
e12187
11.0%
a10996
9.9%
n10346
9.3%
i9411
8.5%
d8102
 
7.3%
7912
 
7.1%
U5376
 
4.8%
S5177
 
4.7%
s5102
 
4.6%
Other values (41)21039
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)110993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t15345
13.8%
e12187
11.0%
a10996
9.9%
n10346
9.3%
i9411
8.5%
d8102
 
7.3%
7912
 
7.1%
U5376
 
4.8%
S5177
 
4.7%
s5102
 
4.6%
Other values (41)21039
19.0%
Distinct1713
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Memory size395.4 KiB
Minimum2008-01-01 00:00:00
Maximum2021-09-25 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-26T14:13:15.662354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:13:16.707455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

release_year
Real number (ℝ)

High correlation 

Distinct74
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.1831
Minimum1925
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size395.4 KiB
2025-11-26T14:13:17.590463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1925
5-th percentile1997
Q12013
median2017
Q32019
95-th percentile2021
Maximum2021
Range96
Interquartile range (IQR)6

Descriptive statistics

Standard deviation8.8241284
Coefficient of variation (CV)0.0043809961
Kurtosis16.224759
Mean2014.1831
Median Absolute Deviation (MAD)2
Skewness-3.4468902
Sum17710712
Variance77.865242
MonotonicityNot monotonic
2025-11-26T14:13:17.946509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20181146
13.0%
20171031
11.7%
20191030
11.7%
2020953
10.8%
2016901
10.2%
2021592
 
6.7%
2015556
 
6.3%
2014352
 
4.0%
2013286
 
3.3%
2012236
 
2.7%
Other values (64)1710
19.4%
ValueCountFrequency (%)
19251
 
< 0.1%
19422
< 0.1%
19433
< 0.1%
19443
< 0.1%
19454
< 0.1%
19462
< 0.1%
19471
 
< 0.1%
19542
< 0.1%
19553
< 0.1%
19562
< 0.1%
ValueCountFrequency (%)
2021592
6.7%
2020953
10.8%
20191030
11.7%
20181146
13.0%
20171031
11.7%
2016901
10.2%
2015556
6.3%
2014352
 
4.0%
2013286
 
3.3%
2012236
 
2.7%

rating
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size395.4 KiB
TV-MA
3205 
TV-14
2157 
TV-PG
861 
R
799 
PG-13
490 
Other values (12)
1281 

Length

Max length8
Median length5
Mean length4.4345502
Min length1

Characters and Unicode

Total characters38993
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowPG-13
2nd rowTV-MA
3rd rowTV-MA
4th rowTV-MA
5th rowTV-MA

Common Values

ValueCountFrequency (%)
TV-MA3205
36.4%
TV-142157
24.5%
TV-PG861
 
9.8%
R799
 
9.1%
PG-13490
 
5.6%
TV-Y7333
 
3.8%
TV-Y306
 
3.5%
PG287
 
3.3%
TV-G220
 
2.5%
NR79
 
0.9%
Other values (7)56
 
0.6%

Length

2025-11-26T14:13:18.604394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tv-ma3205
36.4%
tv-142157
24.5%
tv-pg861
 
9.8%
r799
 
9.1%
pg-13490
 
5.6%
tv-y7333
 
3.8%
tv-y306
 
3.5%
pg287
 
3.3%
tv-g220
 
2.5%
nr79
 
0.9%
Other values (8)59
 
0.7%

Most occurring characters

ValueCountFrequency (%)
-7587
19.5%
V7094
18.2%
T7088
18.2%
M3205
8.2%
A3205
8.2%
12650
 
6.8%
42159
 
5.5%
G1899
 
4.9%
P1638
 
4.2%
R881
 
2.3%
Other values (13)1587
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)38993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
-7587
19.5%
V7094
18.2%
T7088
18.2%
M3205
8.2%
A3205
8.2%
12650
 
6.8%
42159
 
5.5%
G1899
 
4.9%
P1638
 
4.2%
R881
 
2.3%
Other values (13)1587
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)38993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
-7587
19.5%
V7094
18.2%
T7088
18.2%
M3205
8.2%
A3205
8.2%
12650
 
6.8%
42159
 
5.5%
G1899
 
4.9%
P1638
 
4.2%
R881
 
2.3%
Other values (13)1587
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)38993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
-7587
19.5%
V7094
18.2%
T7088
18.2%
M3205
8.2%
A3205
8.2%
12650
 
6.8%
42159
 
5.5%
G1899
 
4.9%
P1638
 
4.2%
R881
 
2.3%
Other values (13)1587
 
4.1%
Distinct220
Distinct (%)2.5%
Missing3
Missing (%)< 0.1%
Memory size395.4 KiB
2025-11-26T14:13:19.341009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length9
Mean length7.0389078
Min length5

Characters and Unicode

Total characters61872
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)0.4%

Sample

1st row90 min
2nd row2 Seasons
3rd row1 Season
4th row1 Season
5th row2 Seasons
ValueCountFrequency (%)
min6126
34.8%
season1791
 
10.2%
11791
 
10.2%
seasons873
 
5.0%
2421
 
2.4%
3199
 
1.1%
90152
 
0.9%
94146
 
0.8%
97146
 
0.8%
93146
 
0.8%
Other values (203)5789
32.9%
2025-11-26T14:13:19.918639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8790
14.2%
n8790
14.2%
m6126
9.9%
i6126
9.9%
16040
9.8%
s3537
 
5.7%
S2664
 
4.3%
o2664
 
4.3%
e2664
 
4.3%
a2664
 
4.3%
Other values (9)11807
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)61872
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8790
14.2%
n8790
14.2%
m6126
9.9%
i6126
9.9%
16040
9.8%
s3537
 
5.7%
S2664
 
4.3%
o2664
 
4.3%
e2664
 
4.3%
a2664
 
4.3%
Other values (9)11807
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)61872
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8790
14.2%
n8790
14.2%
m6126
9.9%
i6126
9.9%
16040
9.8%
s3537
 
5.7%
S2664
 
4.3%
o2664
 
4.3%
e2664
 
4.3%
a2664
 
4.3%
Other values (9)11807
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)61872
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8790
14.2%
n8790
14.2%
m6126
9.9%
i6126
9.9%
16040
9.8%
s3537
 
5.7%
S2664
 
4.3%
o2664
 
4.3%
e2664
 
4.3%
a2664
 
4.3%
Other values (9)11807
19.1%
Distinct513
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size395.4 KiB
2025-11-26T14:13:20.374610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length79
Median length58
Mean length33.417605
Min length6

Characters and Unicode

Total characters293841
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique142 ?
Unique (%)1.6%

Sample

1st rowDocumentaries
2nd rowInternational TV Shows, TV Dramas, TV Mysteries
3rd rowCrime TV Shows, International TV Shows, TV Action & Adventure
4th rowDocuseries, Reality TV
5th rowInternational TV Shows, Romantic TV Shows, TV Comedies
ValueCountFrequency (%)
movies5686
14.5%
tv5485
14.0%
international4101
10.5%
dramas3188
 
8.1%
shows2905
 
7.4%
2607
 
6.7%
comedies2247
 
5.7%
adventure1026
 
2.6%
action1026
 
2.6%
romantic986
 
2.5%
Other values (33)9902
25.3%
2025-11-26T14:13:20.948253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
30366
 
10.3%
e25194
 
8.6%
i21487
 
7.3%
n20747
 
7.1%
a19916
 
6.8%
o19847
 
6.8%
s19585
 
6.7%
t14869
 
5.1%
r14374
 
4.9%
,10504
 
3.6%
Other values (33)96952
33.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)293841
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
30366
 
10.3%
e25194
 
8.6%
i21487
 
7.3%
n20747
 
7.1%
a19916
 
6.8%
o19847
 
6.8%
s19585
 
6.7%
t14869
 
5.1%
r14374
 
4.9%
,10504
 
3.6%
Other values (33)96952
33.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)293841
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
30366
 
10.3%
e25194
 
8.6%
i21487
 
7.3%
n20747
 
7.1%
a19916
 
6.8%
o19847
 
6.8%
s19585
 
6.7%
t14869
 
5.1%
r14374
 
4.9%
,10504
 
3.6%
Other values (33)96952
33.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)293841
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
30366
 
10.3%
e25194
 
8.6%
i21487
 
7.3%
n20747
 
7.1%
a19916
 
6.8%
o19847
 
6.8%
s19585
 
6.7%
t14869
 
5.1%
r14374
 
4.9%
,10504
 
3.6%
Other values (33)96952
33.0%
Distinct8761
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size395.4 KiB
2025-11-26T14:13:22.110680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length248
Median length240
Mean length143.30456
Min length61

Characters and Unicode

Total characters1260077
Distinct characters128
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8734 ?
Unique (%)99.3%

Sample

1st rowAs her father nears the end of his life, filmmaker Kirsten Johnson stages his death in inventive and comical ways to help them both face the inevitable.
2nd rowAfter crossing paths at a party, a Cape Town teen sets out to prove whether a private-school swimming star is her sister who was abducted at birth.
3rd rowTo protect his family from a powerful drug lord, skilled thief Mehdi and his expert team of robbers are pulled into a violent and deadly turf war.
4th rowFeuds, flirtations and toilet talk go down among the incarcerated women at the Orleans Justice Center in New Orleans on this gritty reality series.
5th rowIn a city of coaching centers known to train India’s finest collegiate minds, an earnest but unexceptional student and his friends navigate campus life.
ValueCountFrequency (%)
a11597
 
5.5%
the8135
 
3.9%
to6437
 
3.1%
and6328
 
3.0%
of5265
 
2.5%
in4348
 
2.1%
his3348
 
1.6%
with2264
 
1.1%
her2165
 
1.0%
an1992
 
0.9%
Other values (21442)158132
75.3%
2025-11-26T14:13:24.993780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
201214
16.0%
e118550
 
9.4%
a84626
 
6.7%
t81252
 
6.4%
i78255
 
6.2%
n74433
 
5.9%
o72564
 
5.8%
s72516
 
5.8%
r70675
 
5.6%
h48443
 
3.8%
Other values (118)357549
28.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1260077
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
201214
16.0%
e118550
 
9.4%
a84626
 
6.7%
t81252
 
6.4%
i78255
 
6.2%
n74433
 
5.9%
o72564
 
5.8%
s72516
 
5.8%
r70675
 
5.6%
h48443
 
3.8%
Other values (118)357549
28.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1260077
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
201214
16.0%
e118550
 
9.4%
a84626
 
6.7%
t81252
 
6.4%
i78255
 
6.2%
n74433
 
5.9%
o72564
 
5.8%
s72516
 
5.8%
r70675
 
5.6%
h48443
 
3.8%
Other values (118)357549
28.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1260077
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
201214
16.0%
e118550
 
9.4%
a84626
 
6.7%
t81252
 
6.4%
i78255
 
6.2%
n74433
 
5.9%
o72564
 
5.8%
s72516
 
5.8%
r70675
 
5.6%
h48443
 
3.8%
Other values (118)357549
28.4%

year_added
Real number (ℝ)

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.8727
Minimum2008
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size361.1 KiB
2025-11-26T14:13:25.247692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2016
Q12018
median2019
Q32020
95-th percentile2021
Maximum2021
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5740226
Coefficient of variation (CV)0.00077965422
Kurtosis1.3239044
Mean2018.8727
Median Absolute Deviation (MAD)1
Skewness-0.70582718
Sum17751948
Variance2.4775473
MonotonicityNot monotonic
2025-11-26T14:13:25.359168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
20192016
22.9%
20201879
21.4%
20181648
18.7%
20211498
17.0%
20171186
13.5%
2016428
 
4.9%
201582
 
0.9%
201424
 
0.3%
201113
 
0.1%
201311
 
0.1%
Other values (4)8
 
0.1%
ValueCountFrequency (%)
20082
 
< 0.1%
20092
 
< 0.1%
20101
 
< 0.1%
201113
 
0.1%
20123
 
< 0.1%
201311
 
0.1%
201424
 
0.3%
201582
 
0.9%
2016428
 
4.9%
20171186
13.5%
ValueCountFrequency (%)
20211498
17.0%
20201879
21.4%
20192016
22.9%
20181648
18.7%
20171186
13.5%
2016428
 
4.9%
201582
 
0.9%
201424
 
0.3%
201311
 
0.1%
20123
 
< 0.1%

month_added
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6559763
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size361.1 KiB
2025-11-26T14:13:25.472973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4357539
Coefficient of variation (CV)0.51619082
Kurtosis-1.1814332
Mean6.6559763
Median Absolute Deviation (MAD)3
Skewness-0.061016599
Sum58526
Variance11.804405
MonotonicityNot monotonic
2025-11-26T14:13:25.633565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7827
9.4%
12812
9.2%
9770
8.8%
4764
8.7%
10760
8.6%
8755
8.6%
3741
8.4%
1737
8.4%
6728
8.3%
11705
8.0%
Other values (2)1194
13.6%
ValueCountFrequency (%)
1737
8.4%
2562
6.4%
3741
8.4%
4764
8.7%
5632
7.2%
6728
8.3%
7827
9.4%
8755
8.6%
9770
8.8%
10760
8.6%
ValueCountFrequency (%)
12812
9.2%
11705
8.0%
10760
8.6%
9770
8.8%
8755
8.6%
7827
9.4%
6728
8.3%
5632
7.2%
4764
8.7%
3741
8.4%

age_on_netflix
Real number (ℝ)

High correlation  Zeros 

Distinct75
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6896395
Minimum-3
Maximum93
Zeros3240
Zeros (%)36.8%
Negative14
Negative (%)0.2%
Memory size395.4 KiB
2025-11-26T14:13:26.054168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-3
5-th percentile0
Q10
median1
Q35
95-th percentile22
Maximum93
Range96
Interquartile range (IQR)5

Descriptive statistics

Standard deviation8.788771
Coefficient of variation (CV)1.8740824
Kurtosis16.181226
Mean4.6896395
Median Absolute Deviation (MAD)1
Skewness3.510492
Sum41236
Variance77.242496
MonotonicityNot monotonic
2025-11-26T14:13:26.450218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03240
36.8%
11585
18.0%
2713
 
8.1%
3489
 
5.6%
4367
 
4.2%
5261
 
3.0%
6251
 
2.9%
7187
 
2.1%
8185
 
2.1%
9161
 
1.8%
Other values (65)1354
15.4%
ValueCountFrequency (%)
-31
 
< 0.1%
-21
 
< 0.1%
-112
 
0.1%
03240
36.8%
11585
18.0%
2713
 
8.1%
3489
 
5.6%
4367
 
4.2%
5261
 
3.0%
6251
 
2.9%
ValueCountFrequency (%)
931
 
< 0.1%
761
 
< 0.1%
752
< 0.1%
743
< 0.1%
733
< 0.1%
723
< 0.1%
712
< 0.1%
701
 
< 0.1%
662
< 0.1%
652
< 0.1%

Interactions

2025-11-26T14:13:03.488103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:13:01.916248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:13:02.408091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:13:02.878165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:13:03.650457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:13:02.027115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:13:02.522417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:13:02.988495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:13:03.807570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:13:02.146465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:13:02.644985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:13:03.160686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:13:03.961292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:13:02.274840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:13:02.766090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:13:03.323740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-26T14:13:26.639689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
age_on_netflixmonth_addedratingrelease_yeartypeyear_added
age_on_netflix1.000-0.0830.143-0.8510.1850.051
month_added-0.0831.0000.039-0.0390.015-0.162
rating0.1430.0391.0000.1360.3420.096
release_year-0.851-0.0390.1361.0000.1670.373
type0.1850.0150.3420.1671.0000.062
year_added0.051-0.1620.0960.3730.0621.000

Missing values

2025-11-26T14:13:04.215843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-26T14:13:04.490276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescriptionyear_addedmonth_addedage_on_netflix
0s1MovieDick Johnson Is DeadKirsten JohnsonUnknownUnited States2021-09-252020PG-1390 minDocumentariesAs her father nears the end of his life, filmmaker Kirsten Johnson stages his death in inventive and comical ways to help them both face the inevitable.202191
1s2TV ShowBlood & WaterUnknownAma Qamata, Khosi Ngema, Gail Mabalane, Thabang Molaba, Dillon Windvogel, Natasha Thahane, Arno Greeff, Xolile Tshabalala, Getmore Sithole, Cindy Mahlangu, Ryle De Morny, Greteli Fincham, Sello Maake Ka-Ncube, Odwa Gwanya, Mekaila Mathys, Sandi Schultz, Duane Williams, Shamilla Miller, Patrick MofokengSouth Africa2021-09-242021TV-MA2 SeasonsInternational TV Shows, TV Dramas, TV MysteriesAfter crossing paths at a party, a Cape Town teen sets out to prove whether a private-school swimming star is her sister who was abducted at birth.202190
2s3TV ShowGanglandsJulien LeclercqSami Bouajila, Tracy Gotoas, Samuel Jouy, Nabiha Akkari, Sofia Lesaffre, Salim Kechiouche, Noureddine Farihi, Geert Van Rampelberg, Bakary DiomberaUnited States2021-09-242021TV-MA1 SeasonCrime TV Shows, International TV Shows, TV Action & AdventureTo protect his family from a powerful drug lord, skilled thief Mehdi and his expert team of robbers are pulled into a violent and deadly turf war.202190
3s4TV ShowJailbirds New OrleansUnknownUnknownUnited States2021-09-242021TV-MA1 SeasonDocuseries, Reality TVFeuds, flirtations and toilet talk go down among the incarcerated women at the Orleans Justice Center in New Orleans on this gritty reality series.202190
4s5TV ShowKota FactoryUnknownMayur More, Jitendra Kumar, Ranjan Raj, Alam Khan, Ahsaas Channa, Revathi Pillai, Urvi Singh, Arun KumarIndia2021-09-242021TV-MA2 SeasonsInternational TV Shows, Romantic TV Shows, TV ComediesIn a city of coaching centers known to train India’s finest collegiate minds, an earnest but unexceptional student and his friends navigate campus life.202190
5s6TV ShowMidnight MassMike FlanaganKate Siegel, Zach Gilford, Hamish Linklater, Henry Thomas, Kristin Lehman, Samantha Sloyan, Igby Rigney, Rahul Kohli, Annarah Cymone, Annabeth Gish, Alex Essoe, Rahul Abburi, Matt Biedel, Michael Trucco, Crystal Balint, Louis OliverUnited States2021-09-242021TV-MA1 SeasonTV Dramas, TV Horror, TV MysteriesThe arrival of a charismatic young priest brings glorious miracles, ominous mysteries and renewed religious fervor to a dying town desperate to believe.202190
6s7MovieMy Little Pony: A New GenerationRobert Cullen, José Luis UchaVanessa Hudgens, Kimiko Glenn, James Marsden, Sofia Carson, Liza Koshy, Ken Jeong, Elizabeth Perkins, Jane Krakowski, Michael McKean, Phil LaMarrUnited States2021-09-242021PG91 minChildren & Family MoviesEquestria's divided. But a bright-eyed hero believes Earth Ponies, Pegasi and Unicorns should be pals — and, hoof to heart, she’s determined to prove it.202190
7s8MovieSankofaHaile GerimaKofi Ghanaba, Oyafunmike Ogunlano, Alexandra Duah, Nick Medley, Mutabaruka, Afemo Omilami, Reggie Carter, MzuriUnited States, Ghana, Burkina Faso, United Kingdom, Germany, Ethiopia2021-09-241993TV-MA125 minDramas, Independent Movies, International MoviesOn a photo shoot in Ghana, an American model slips back in time, becomes enslaved on a plantation and bears witness to the agony of her ancestral past.2021928
8s9TV ShowThe Great British Baking ShowAndy DevonshireMel Giedroyc, Sue Perkins, Mary Berry, Paul HollywoodUnited Kingdom2021-09-242021TV-149 SeasonsBritish TV Shows, Reality TVA talented batch of amateur bakers face off in a 10-week competition, whipping up their best dishes in the hopes of being named the U.K.'s best.202190
9s10MovieThe StarlingTheodore MelfiMelissa McCarthy, Chris O'Dowd, Kevin Kline, Timothy Olyphant, Daveed Diggs, Skyler Gisondo, Laura Harrier, Rosalind Chao, Kimberly Quinn, Loretta Devine, Ravi KapoorUnited States2021-09-242021PG-13104 minComedies, DramasA woman adjusting to life after a loss contends with a feisty bird that's taken over her garden — and a husband who's struggling to find a way forward.202190
show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescriptionyear_addedmonth_addedage_on_netflix
8797s8798TV ShowZak StormUnknownMichael Johnston, Jessica Gee-George, Christine Marie Cabanos, Christopher Smith, Max Mittelman, Reba Buhr, Kyle HebertUnited States, France, South Korea, Indonesia2018-09-132016TV-Y73 SeasonsKids' TVTeen surfer Zak Storm is mysteriously transported to the Bermuda Triangle, where he becomes the captain of a magical ship full of misfits.201892
8798s8799MovieZed PlusChandra Prakash DwivediAdil Hussain, Mona Singh, K.K. Raina, Sanjay Mishra, Anil Rastogi, Ravi Jhankal, Kulbhushan Kharbanda, Ekavali Khanna, Mukesh Tiwari, Vinod AcharyaIndia2019-12-312014TV-MA131 minComedies, Dramas, International MoviesA philandering small-town mechanic's political ambitions are sparked when the visiting prime minister mistakenly grants him special security clearance.2019125
8799s8800MovieZendaAvadhoot GupteSantosh Juvekar, Siddharth Chandekar, Sachit Patil, Chinmay Mandlekar, Rajesh Shringarpure, Pushkar Shrotri, Tejashree Pradhan, Neha JoshiIndia2018-02-152009TV-14120 minDramas, International MoviesA change in the leadership of a political party sparks bitter conflict and the party's division into two rival factions.201829
8800s8801TV ShowZindagi Gulzar HaiUnknownSanam Saeed, Fawad Khan, Ayesha Omer, Mehreen Raheel, Sheheryar Munawar, Samina Peerzada, Waseem Abbas, Javed Sheikh, Hina Khawaja BayatPakistan2016-12-152012TV-PG1 SeasonInternational TV Shows, Romantic TV Shows, TV DramasStrong-willed, middle-class Kashaf and carefree, wealthy Zaroon meet in college, but before love can take root, they each have some growing up to do.2016124
8801s8802MovieZinzanaMajid Al AnsariAli Suliman, Saleh Bakri, Yasa, Ali Al-Jabri, Mansoor Alfeeli, AhdUnited Arab Emirates, Jordan2016-03-092015TV-MA96 minDramas, International Movies, ThrillersRecovering alcoholic Talal wakes up inside a small-town police station cell, where he's subject to the mind games of a psychotic sadist.201631
8802s8803MovieZodiacDavid FincherMark Ruffalo, Jake Gyllenhaal, Robert Downey Jr., Anthony Edwards, Brian Cox, Elias Koteas, Donal Logue, John Carroll Lynch, Dermot Mulroney, Chloë SevignyUnited States2019-11-202007R158 minCult Movies, Dramas, ThrillersA political cartoonist, a crime reporter and a pair of cops investigate San Francisco's infamous Zodiac Killer in this thriller based on a true story.20191112
8803s8804TV ShowZombie DumbUnknownUnknownUnited States2019-07-012018TV-Y72 SeasonsKids' TV, Korean TV Shows, TV ComediesWhile living alone in a spooky town, a young girl befriends a motley crew of zombie children with diverse personalities.201971
8804s8805MovieZombielandRuben FleischerJesse Eisenberg, Woody Harrelson, Emma Stone, Abigail Breslin, Amber Heard, Bill Murray, Derek GrafUnited States2019-11-012009R88 minComedies, Horror MoviesLooking to survive in a world taken over by zombies, a dorky college student teams with an urban roughneck and a pair of grifter sisters.20191110
8805s8806MovieZoomPeter HewittTim Allen, Courteney Cox, Chevy Chase, Kate Mara, Ryan Newman, Michael Cassidy, Spencer Breslin, Rip Torn, Kevin ZegersUnited States2020-01-112006PG88 minChildren & Family Movies, ComediesDragged from civilian life, a former superhero must train a new crop of youthful saviors when the military preps for an attack by a familiar villain.2020114
8806s8807MovieZubaanMozez SinghVicky Kaushal, Sarah-Jane Dias, Raaghav Chanana, Manish Chaudhary, Meghna Malik, Malkeet Rauni, Anita Shabdish, Chittaranjan TripathyIndia2019-03-022015TV-14111 minDramas, International Movies, Music & MusicalsA scrappy but poor boy worms his way into a tycoon's dysfunctional family, while facing his fear of music and the truth about his past.201934